Lung nodule size and growth rates are strong predictors of malignancy (and are used to distinguish benign from malignant). The determination of nodule size involves the manual outlining of a nodule's boundary. This is a tedious task due to the complex shape of nodules and that the nodule spans multiple slices. As a result boundary outlines are subject to individual radiologist interpretation and can lead to large inter-observer variation of the nodule's size estimate.
A method of automating the process is to have a computer perform such a task once the lesion has been identified. This task is commonly referred to in the image-processing domain as image or volume segmentation and techniques referred to as region growing are typically applied. Region growing algorithms typically use local image characteristics, such as image intensity variations to decide whether a neighboring voxel (3D volume images) or pixel (2D planar images) is to be added to the growing region. Nodules are frequently attached to other normal anatomy structures, including the local pulmonary vasculature and the pleural surface adjoining the thoracic wall. Thus, segmenting the lesion from normal anatomy is a difficult task as the image differences between the lesion and normal anatomy often are not discernable in terms of voxel intensity values, e.g., Hounsfield units HU. As a consequence, region-growing tasks often expand beyond the target and, in the case of segmenting lesions, include regions that are normal anatomy.
In addition, many pulmonary nodules are either part-solid, composed of a solid center surrounded by a diffuse cloud or are non-solid. It is often desirable to be able to quantify the proportion of solid and non-solid components in the nodules. The choice of the Hounsfield unit threshold used for segmenting theses types of nodule is a crucial parameter. Too high a threshold leads to an under segmentation of the nodule and an underestimation of the nodule's volume. As the Hounsfield threshold is lowered the number and complexity of attached vessels increases and the nodule can become attached to other structures. As a result it is harder to segment the nodule from the normal anatomy and consequently more sophisticated segmentation algorithms are required.
One problem with known volumetric segmentation methods is the tendency to include part of the normal anatomy with the detected nodule, because of an inability to distinguish between the two. As mentioned before using a low enough Hounsfield threshold to capture the non-solid component of a nodule exacerbates this problem. To avoid this consequence many methods use Hounsfield threshold suitable for segmenting only the solid component. Thus there is a need for a volumetric segmentation method that can segment both the solid and non-solid components of a nodule.
Another problem with known volumetric segmentation methods stems from the use of Hounsfield thresholds to distinguish between target structures such as, for example, nodules or lesions, and anatomical structures such as, for example, local pulmonary vasculature or the pleural surface adjoining the thoracic wall. The difference, in Hounsfield units, between a target structure and a surrounding anatomical structure is very small. Thus, when segmenting a target structure disposed proximate an anatomical structure, a relatively high Hounsfield threshold must be used to distinguish between the target structure and the anatomical structure. Segmenting at such a relatively high threshold, however, may not allow a specialist to determine the full extent of the target structure. Similarly, although a relatively low Hounsfield threshold can be used to determine a greater extent of the target structure, segmenting at such a relatively low threshold may not allow a specialist to distinguish between the target structure and the surrounding anatomical structure.
It is the object of the presence invention to provide an improved volumetric segmentation method for nodules from a three-dimensional volume data. By providing the user with a plurality of conservative to aggressive volumetric segmentations that progressively includes more non-solid component. The present invention approaches this problem by using a multi-growth stage segmentation process.